• International Journal of Ocean System Engineering
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• v.1 no.2
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• pp.60-67
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• 2011

This paper presents an in-situ correction method to compensate for the position error of an autonomous underwater vehicle (AUV) near the sea floor. AUVs generally have an inertial navigation system assisted with auxiliary navigational sensors. Since the inertial navigation system shows drift in position without the bottom reflection of a Doppler velocity log, external acoustic positioning systems, such as an ultra short baseline (USBL), are needed to set the position without surfacing the AUV. The main concept of the correction method is as follows: when the AUV arrives near the sea floor, the vehicle moves around horizontally in a circular mode, while the USBL transceiver installed on a surface vessel measures the AUV's position. After acquiring one data set, a least-square curve fitting method is adopted to find the center of the AUV's circular motion, which is transferred to the AUV via an acoustic telemetry modem (ATM). The proposed method is robust for the outlier of USBL, and it is independent of the time delay for the data transfer of the USBL position with the ATM. The proposed method also reduces the intrinsic position error of the USBL, and is applicable to the in-situ calibration as well as the initialization of the AUVs' position. Monte Carlo simulation was conducted to verify the effectiveness of the method.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.508-510
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• 2004

In this paper, we proposed the GPS position data correction method for autonomous land navigation using vanishing point property and a monocular vision system. Simulations are carried out over driving distances of approximately 60 km on the basis of realistic road data. In straight road, the proposed method reduces GPS position error to minimum more than 63% and positioning errors within less than 0.5m are observed. However, the average accuracy of the method is not presented. because it is difficult to estimate it in curve road or other road environments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.187-193
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• 2004

In this paper, we proposed the GPS position data correction method for autonomous land navigation using vanishing point property and a monocular vision system. Simulations are carried out over driving distances of approximately 60 km on the basis of realistic road data. On a straight road, the proposed method reduces GPS position error by at least 63% within 0.5 m. However, the average accuracy of the method is not presented, because it is difficult to estimate it on other than a straight road in variable conditions.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.33-39
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• 2020

This paper presents an Around View Monitoring (AVM) stop-line detection based longitudinal position correction algorithm for automated driving on urban roads. Poor positioning accuracy of low-cost GPS has many problems for precise path tracking. Therefore, this study aims to improve the longitudinal positioning accuracy of low-cost GPS. The algorithm has three main processes. The first process is a stop-line detection. In this process, the stop-line is detected using Hough Transform from the AVM camera. The second process is a map matching. In the map matching process, to find the corrected vehicle position, the detected line is matched to the stop-line of the HD map using the Iterative Closest Point (ICP) method. Third, longitudinal position of low-cost GPS is updated using a corrected vehicle position with Kalman Filter. The proposed algorithm is implemented in the Robot Operating System (ROS) environment and verified on the actual urban road driving data. Compared to low-cost GPS only, Test results show the longitudinal localization performance was improved.

• The Journal of Korean Physical Therapy
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• v.31 no.3
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• pp.151-156
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• 2019

Purpose: This study aimed to investigate the immediate effects of posture correction and real-time visual feedback using a video display on muscle activity and change of head position during overhead arm lift test in individuals with forward head posture. Methods: Fifteen subjects with forward head posture and fifteen normal subjects who volunteered were included in this study. During both groups performed the overhead arm lift test, the muscle activity of the upper trapezius, serratus anterior, sternocleidomastoid, and lower trapezius muscle were measured using electromyography, and head position change was measured using photographs. Then, forward head posture group was asked to perform overhead arm lift test again after posture correction and real-time visual feedback using a video display respectively. One-way analysis of variance (ANOVA) was used to analyze four conditions: pre-test, posture correction, real-time visual feedback, and the control group. Results: The upper trapezius and lower trapezius muscle activity significantly decreased posture correction, real-time visual feedback, and control group than pre-test of forward head posture group (p<0.05). The sternocleidomastoid muscle significantly decreased real-time visual feedback and control group than pre-test of forward head posture group. Head position change significantly decreased three conditions than pre-test of forward head posture group and real-time visual feedback and control group significantly decreased than posture correction. Conclusion: This study recommend for maintaining cervical stability during the overhead arm lift test, postural control using real-time visual feedback is more effective in subjects with forward head posture.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.119-124
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• 2013

Recent increasing demand on the indoor localization requires more advanced and hybrid technology. This paper proposes an application of the hybrid indoor localization method based on a position-coded pattern that can be used with other existing indoor localization techniques such as vision, beacon, or landmark technique. To reduce the pattern-recognition error rate, the error detection and correction algorithm was applied based on Hamming code. The indoor localization experiments based on the proposed algorithm were performed by using a QCIF-grade CMOS sensor and a position-coded pattern with an area of $1.7{\times}1.7mm^2$. The experiments have shown that the position recognition error ratio was less than 0.9 % with 0.4 mm localization accuracy. The results suggest that the proposed method could be feasibly applied for the localization of the indoor mobile service robots.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.9
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• pp.151-156
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• 2002

A new position correction method using four fiducials as reference points was developed and examined. It was aimed to calibrate nonlinear deformation by numerous error sources. A correlation for correction was derived from the geometric relationship between four fiducials and chip position. Compared with three points method, it exhibited more accurate correction, especially for inner area of a quadrilateral composed of four fiducial points. Its accuracy was found to be increased as fiducials moves outwardly within a printed circuit board (PCB) and/or as they form more rectangle-like shape As for arbitrarily nonlinear deformation, this method can be applied using more than five fiducials. In this case, local-area calibration is carried out by sectioning a board area into several rectangular are as.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.4
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• pp.391-398
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• 2023

In this paper, we propose a Correction Dead Reckoning (CDR) solution using correction information such as Map Matching FeedBack (MMFB) in an underground parking lot. In order to correct position errors in an underground parking lot, vehicle position and heading errors are corrected using MMFB information in road link properties. The proposed method was applied to an in-vehicle navigation system and tested. The experimental results show that the proposed robust dead reckoning solution corrects Dead Reckoning (DR) position errors that occur when driving for a long time in an underground parking lot.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.155-158
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• 2024

Multipath, a major error source in urban GNSS positioning (global navigation satellite system), pose a challenge due to its site-dependent nature, varying with the user's signal reception environment. In our previous study, we introduced a technique generating GNSS multipath map in urban canyon. However, due to uncertainty in initial GNSS positions, applying multipath maps required generating multiple candidate positions. In this study, we present an efficient method for applying multipath maps by projecting the multipath correction in position domain. This approach effectively applies multipath maps, addressing the challenges posed by urban user position uncertainties.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4971-4987
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• 2019

Node localization is the basic task of underwater wireless sensor networks (UWSNs). Most of the existing underwater localization methods rely on ranging accuracy. Due to the special environment conditions in the ocean, beacon nodes are difficult to deploy accurately. The narrow bandwidth and high delay of the underwater acoustic communication channel lead to large errors. In order to reduce the ranging error and improve the positioning accuracy, we propose a localization algorithm based on ranging correction and inertial coordination. The algorithm can be divided into two parts, Range Correction based Localization algorithm (RCL) and Inertial Coordination based Localization algorithm (ICL). RCL uses the geometric relationship between the node positions to correct the ranging error and obtain the exact node position. However, when the unknown node deviates from the deployment area with the movement of the water flow, it cannot communicate with enough beacon nodes in a certain period of time. In this case, the node uses ICL algorithm to combine position data with motion information of neighbor nodes to update its position. The simulation results show that the proposed algorithm greatly improves the positioning accuracy of unknown nodes compared with the existing localization methods.